从传统协作到大模型驱动，多智能体系统迎来范式升级
Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
- 以闭环协同框架分析传统多智能体系统的感知、通信、决策与控制
- 引入大基础模型实现语义级推理，提升跨场景适应能力
- 适合关注AI协作架构演进的研究者与工程实践者
随着人工智能的快速发展，多智能体系统（MASs）正从经典范式向基于大基础模型（LFMs）的架构演进。本文系统综述并对比分析了经典多智能体系统（CMASs）与基于大基础模型的多智能体系统（LMASs）。首先，在闭环协同框架下，从感知、通信、决策与控制四个基本维度回顾了CMASs。超越该框架，LMASs融合大基础模型，将协作从低层级状态交换提升至语义级推理，实现更灵活的协调与更强的多样性场景适应性。随后，从架构、运行机制、适应性与应用等维度进行对比分析。最后，展望多智能体系统的未来，总结开放挑战与潜在研究方向。
原文摘要 · Abstract (English)
With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFMs). This survey provides a systematic review and comparative analysis of classical MASs (CMASs) and LFM-based MASs (LMASs). First, within a closed-loop coordination framework, CMASs are reviewed across four fundamental dimensions: perception, communication, decision-making, and control. Beyond this framework, LMASs integrate LFMs to lift collaboration from low-level state exchanges to semantic-level reasoning, enabling more flexible coordination and improved adaptability across diverse scenarios. Then, a comparative analysis is conducted to contrast CMASs and LMASs across architecture, operating mechanism, adaptability, and application. Finally, future perspectives on MASs are presented, summarizing open challenges and potential research opportunities.
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